Bistable State Switch Enables Ultrasensitive Feedback Control in Heterogeneous Microbial Populations

Abstract

Molecular feedback control circuits can improve robustness of gene expression at the single cell-level. This achievement can be offset by requirements of rapid protein expression, that may induce cellular stress, known as burden, that reduces colony growth. To begin to address this challenge we take inspiration by 'division-of-labor' in heterogeneous cell populations: we propose to combine bistable switches and quorum sensing systems to coordinate gene expression at the population-level. We show that bistable switches in individual cells operating in parallel yield an ultrasensitive response, while cells maintain heterogeneous levels of gene expression to avoid burden across all cells. Within a feedback loop, these switches can achieve robust reference tracking and adaptation to disturbances at the population-level. We also demonstrate that molecular sequestration enables tunable hysteresis in individual switches, making it possible to obtain a wide range of stable population-level expressions.

Building subtraction operators and controllers via molecular sequestration

“…This question is difficult to answer in the native context of the cell, but there is evidence that sequestration enables the computation of the difference in the level of participating molecules [1], [2]. In synthetic biology, mathematical modeling has shown how to leverage this property to build molecular controllers [3]- [6], networks for frequency modulation [7], bistable switches with tunable hysteresis [8], [9], gradient detectors [10], [11], and biomolecular perceptrons [12]; some of these ideas have been experimentally demonstrated [13]- [16]. It is abundantly evident that sequestration helps compute the difference of two inputs at steady-state [1]- [4], and linear analysis shows that it works as a sum junction [17].…”

Model predictive control of cancer cellular dynamics: a new strategy for therapy design

“…Three main approaches have been proven to be effective for the control of different processes (such as gene expression and cell proliferation), namely: i) open-or closed-loop controllers embedded into cells by means of synthetic gene networks (Bloom et al, 2015;Hsiao et al, 2015;Briat et al, 2016;Ciar et al, 2018;Aoki et al, 2019;Pedone et al, 2019;Ye et al, 2016;Andrews et al, 2018;Gao et al, 2018;Siu et al, 2018;Bashor et al, 2019;Cuba Samaniego and Franco, 2021;Shakiba et al, 2021); ii) external controllers, where the controlled processes are within cells, while the controller (either at single cell or cellpopulation level) and the actuation functions are implemented externally via microfluidics-optogenetics/microscopy-flow cytometry platforms and adequate algorithms for online cell output quantification and control (Milias-Argeitis et al, 2011;Toettcher et al, 2011;Uhlendorf et al, 2012;Menolascina et al, 2014;Lugagne et al, 2017;Postiglione et al, 2018;Khazim et al, 2019;Shannon et al, 2020;de Cesare et al, 2021;Khazim et al, 2021;Pedone et al, 2021;de Cesare et al, 2022); iii) multicellular strategies, where both the control and actuation functions are embedded into cellular consortia (Matyjaszkiewicz et al, 2017;Fiore et al, 2016;Fiore et al, 2017;Kylilis et al, 2018;Postiglione et al, 2019;Ren et al, 2021). Plenty of examples of embedded controllers have been engineered across different cellular chassis; instead, applications of external and multicellular controllers in mammalian cells are scarce and either just theoretical or limited to proofs of concept.…”